* Computational models of human language acquisition and processing,
models of language evolution and change, and simulation and analysis
of psycholinguistic findings.

Special Topic
--------------------------
With the recent growing interest in statistical approaches to natural
language learning that go beyond linear models and convex
optimization, e.g., latent variable models or deep learning
approaches, including recurrent and recursive networks, we are
especially interested in papers dealing with theoretical or empirical
analyses and of such models and their learning algorithms. This
includes analysis of non-convex learning in the context of language
processing, as well as theoretical or empirical results on phenomena
that can and cannot be learned well with a given approach.

Submission
----------------------
Submissions to CoNLL-2015 must describe original, unpublished work in
8 pages of content plus 2 additional pages of references. Papers will
be presented orally, or as posters with a five minute oral
presentation (poster booster). All papers will be published in the
conference proceedings. Submission details will be provided online at
http://www.conll.org/ in time.

Best Paper Award
---------------------------------
As in recent CoNLL conferences, a Best Paper Award will be given to
the authors of the highest quality paper. The most important aspects
in judging the quality of a paper will be: originality,
innovativeness, relevance, and impact of the presented research.

Shared Task
-----------------------
The 2016 CoNLL Shared Task will be a follow-on to the 2015 Shared Task
on Shallow Discourse Parsing (SDP). This 2nd edition will be
multilingual. In addition to English, which will be a repeat of the
2015 Shared Task, it will also include Chinese. Registration will
start in mid-January, 2016, when the training data and scorer will
also be available. The evaluation window will be in late
April. Shared task results will be presented at the CoNLL conference
in Berlin.

For more information and important dates of the shared task
competition see http://www.cs.brandeis.edu/~clp/conll16st/